ALPR is an image-processing approach that often functions as the core module of “intelligent” transportation infrastructure applications. License plate recognition techniques, such as ALPR, can be employed to identify a vehicle by automatically reading a license plate utilizing image processing and character recognition technologies. A license plate recognition operation can be performed by locating a license plate in an image, segmenting the characters in the captured image of the plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified.
The ALPR problem is often decomposed into a sequence of image processing operations: locating the sub-image containing the license plate (i.e., plate localization), extracting images of individual characters (i.e., segmentation), and performing optical character recognition (OCR) on these character images. In order for OCR to achieve high accuracy, it is necessary to obtain properly segmented characters.
FIG. 1, for example, illustrates a block diagram of a prior art ALPR system 10. ALPR system 10 generally includes an image capture module 12 that provides data (e.g., an image) to a license plate localization module 14. Output from module 14 is input to a character segmentation module 16, which in turn outputs data that is input to a character recognition module 18. Data output from the character recognition module 18 is provided as input to a state identification module 20.
There are a number of challenging noise sources present in license plate images captured under realistic conditions (i.e., field deployed solutions). These include: heavy shadows, non-uniform illumination (from one vehicle to the next, daytime versus nighttime, etc.), challenging optical geometries (tilt, shear, or projective distortions), plate frames and/or stickers partially touching characters, partial occlusion of characters (e.g., trailer hitch ball), poor contrast, and general image noise (e.g., salt and pepper noise). For ALPR systems deployed in the USA, for example, variations between states in character font, width, and spacing further add to the difficulty of proper character segmentation.